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Learning the Relevant Percepts of Modular Hierarchical Bayesian Driver Models Using a Bayesian Information Criterion

  • Mark Eilers
  • Claus Möbus
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6777)

Abstract

Modeling drivers’ behavior is essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control-theoretic and production-system models. Based on psychological studies various perceptual measures (angles, distances, time-to-x-measures) have been proposed for such models. These proposals are partly contradictory and depend on special experimental settings. A general computational vision theory of driving behavior is still pending. We propose the selection of drivers’ percepts according to their statistical relevance. In this paper we present a new machine-learning method based on a variant of the Bayesian Information Criterion (BIC) using a parent-child-monitor to obtain minimal sets of percepts which are relevant for drivers’ actions in arbitrary scenarios or maneuvers.

Keywords

Probabilistic Driver model Bayesian Autonomous Driver model Mixture-of-Behavior model Bayesian Real-Time-Control Machine-Learning Bayesian Information Criterion Hierarchical Bayesian Models 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mark Eilers
    • 1
  • Claus Möbus
    • 1
  1. 1.Transportation Systems, Learning and Cognitive Systems OFFIS Institute for Information TechnologyC.v.O. UniversityOldenburgGermany

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